Real-Time Predictive and Prescriptive Analytics in Information Systems: Theory and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2165
Special Issue Editor
Interests: Industry 4.0; intelligent systems; management of information systems; predictive and prescriptive analytics; real-time decision-making; proactive and event-driven computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
During the last years, due to the advancements of the Internet of Things, there has been an explosion of research works dealing with predictive analytics, aiming to determine what will occur and the reason for this in the future of several domains. Alongside this, prescriptive analytics has now begun to produce algorithms that aim to tackle questions about what should be done and why it should be. These trends are being facilitated by the availability of sensor-generated big data, providing the capability for real-time processing and insights.
The purpose of this Special Issue is to provide algorithms and methods for real-time predictive and prescriptive analytics in order to facilitate real-time decision making, taking into account the requirements derived from their incorporation into data-driven information systems. In this sense, research works dealing with Machine Learning, Deep Learning, and Reinforcement Learning for predictive and prescriptive analytics lay within the scope of this publication. Conceptual and technical architectures of information systems which are capable of supporting the proposed algorithms and methods are also relevant for this inclusion in this Special Issue. The proposed approaches, methods, algorithms, and architectures should incorporate a solid validation and results.
The scope of this Special Issue includes, but is not limited to, the following topics:
- predictive analytics;
- prescriptive analytics;
- machine learning;
- deep learning;
- reinforcement learning;
- big data architectures;
- artificial intelligence;
- real-time decision making.
Dr. Alexandros Bousdekis
Guest Editor
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